Interested in this AI/ML Engineer role at Rootstock Software?
Apply Now →Skills & Technologies
About This Role
AI Engineering Systems Lead
-------------------------------
### Most engineering orgs are bolting AI onto how they already work. We're rebuilding the work itself.
We believe most of the code in our platform is going to be authored by AI, and that this changes the job of architecture, not just the speed of typing. LLMs are brilliant at producing code that looks right and runs on the first try. They are unreliable at remembering the invariants that keep a large system correct. A platform that only works when the author remembers the rules will quietly rot under AI authorship.
So we made a different bet. We build the machinery that makes the rules impossible to break: deterministic checks, compile\-time types, runtime guardrails, and autonomous pipelines that ship code through all of it. We're well into this work. We need someone to own it and push it to the frontier.
Why Rootstock, why now:Rootstock brings proven depth in ERP and manufacturing — real products, real customers, decades of hard\-won domain knowledge. We're putting that depth behind a clear blueprint for becoming AI Native, with active development underway and the funding to see it through. You'd build alongside a deep bench of senior engineers who have shipped real ERP at scale. Iron sharpens iron.
The role in one line:You own the internal systems that let Rootstock engineers — and our agents — ship production Salesforce code fast and ship it correct.
What you'll actually build
------------------------------
This isn't a greenfield daydream. There's real, running scaffolding for you to take ownership of and extend:
- Autonomous coding pipelines. A bugfix pipeline already turns a Jira ticket into a reviewed Apex/LWC pull request, with an observability layer that scores every run. You'll deepen it, widen it to new pipeline types, and raise the bar on how autonomously and reliably it ships.
- A root\-cause engine, and the evals that keep it honest. One of our most important tools investigates a bug across multiple repos and millions of lines of code to find the actual root cause, grounded in a continuously built specification corpus. Evals run constantly — we pit the current generation against candidate generations and against fixes real engineers actually shipped. You'll own it and keep pushing it forward.
- A 'Constitution' of enforced architecture. We maintain foundational architectural decision records that bind every repo, on a single thesis: every principle must have a deterministic enforcement seam — a type, a startup check, a CI test, or a hook — never just a doc. You'll extend this and the runtime that enforces it across the Claude Code session lifecycle.
- TDD tooling that actually changes behavior. A local in\-memory Apex test runner lets engineers run the red\-green\-refactor loop without deploying to an org. You'll build the tooling that makes test\-first the path of least resistance, for humans and agents alike.
- The guardrails. A suite of quality and safety hooks that format, scan, and block bad edits before they land, plus the PR gate that runs the full scanner suite. You'll decide what the next generation looks like.
What we're looking for
--------------------------
- A strong systems and types thinker. You reach for the type system, the lint rule, or the CI test to make a class of bugs unrepresentable, rather than writing a paragraph in a style guide.
- Real fluency with AI\-assisted and agentic development. You've built with agents, you have opinions about where they break, and you've designed around their failure modes.
- Solid programming fundamentals. Competence in Java, JavaScript, or something comparable translates well — strong engineers pick up the rest.
- Comfort building developer tooling, CI/CD, and orchestration: hooks, pipelines, observability. You like making other engineers faster.
- Salesforce and Apex experience is a plus, not a gate. We care more about how you think about platforms than how many years you've shipped on any one of them.
You'll thrive here if…
--------------------------
- You build things for fun. Have you ever shipped a side project just because you wanted to see if it would work? We want to hear about it.
- You enjoy experiment, trial, failure, and rapid iteration — and you instrument your way out rather than freezing when the first attempt fails.
- You've built a proof of concept just to test a theory, and killed one just as happily when the data said no.
- You're a self\-directed learner who's eager to leave a real mark on how an organization builds software. There is enormous surface area here and very little bureaucracy in your way.
### How we work
We do genuinely new things, and new things fail often. We expect to chase ten ideas and have seven hit dead ends. Nobody gets punished for a smart bet that didn't pay off — the job is to run the experiment, learn from it, share what you found, and keep moving. We write up our dead ends openly, because a well\-understood failure shapes the next decision as much as a win does, often more.
We care as much about raising the whole team as we do about shipping the next tool. People here treat helping a teammate level up on AI as the fun part, not a tax on their time. No egos. We push each other to get sharper.
### What we'll go deep on in interviews
Our interviews are not trivia. Expect to go deep on models and their tradeoffs, the AI\-assisted systems you've built, the hardest problems you've hit building with AI, eval and measurement, and where you think agentic coding is actually going. Bring your strong opinions and your scars.
The basics
--------------
- Location: Remote, US\-based
- Visa Sponsorship: This position requires authorization to work in the US; we are unable to provide visa sponsorship, including H\-1B.
- Compensation: Base salary, annual bonus, and a long\-term incentive plan (LTIP). We set the level by experience — show us what you've built. Your work has a direct line to the LTIP upside.
- Tooling: Claude license, full access to Salesforce's AI tooling, approved API spend, and budget for local model experimentation. We don't want cost to be the reason you didn't try something.
- How to apply: Send us the work — repos, demos, write\-ups, the proof of concept you couldn't stop tinkering with. A link to something real is worth more than a polished résumé. Please include your desired salary range.
Rootstock Software is an equal\-opportunity employer. We evaluate all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected\-veteran status, or any other characteristic protected by law.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rootstock Software, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rootstock Software AI Hiring
Rootstock Software has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.